A new age of web chat was launched by the pandemic, plunging us into a virtual cocoon that allows the convenience of working and networking from home. Even full-blown conferences were transformed from in vivo to in silico.
Many of us have learned to love our virtual cocoons, leading to the question, “Why go back?” Why put up with commuting, traveling, and putting on pants again? Our virtual cocoons are ideal: all the work with all the flexibility — and no downside, right?
The downside of virtuality: Comfort zone mentality
Attending a conference from home is a double-edged sword. The upside is comfort and convenience, but the downside is also comfort and convenience. In our virtual cocoon, we easily webchat and email familiar colleagues, but how often are we facilitating meaningful new connections? We comfortably present our research over Zoom, but are we practicing good presentation skills or going through the motions? It’s convenient to attend a talk mid-day without leaving the lab or the living room, but how often are we giving speakers our undivided attention?
Benefits of in-person conferences
I recently had the pleasure of attending The Biophysical Society conference in San Francisco, my first post-pandemic conference. As a senior PhD student pursuing a career outside of academia, I was apprehensive that I’d benefit from an in-person academic research conference. I was wrong. The pros about in-person conferences outweigh the convenience of virtual networking:
With vaccinations, masking, and social distancing, in-person meetings are becoming safe again. It’s tempting to never emerge from our virtual cocoons, but if we remain in our cocoons, how will we grow?
After attending the BPS conference, I realized that, yes, my virtual world had brought convenience and opportunity. But it also placed me in a comfort zone. In my cocoon, I am the sole motivator for who I speak, listen, and network with. But at the conference, I interacted with early and late-career scientists I would not have had the pleasure to meet online. Am I anti-zoom? Let’s not go that far. But let’s never forget the benefits of in-person socialization.
Megalodon is an extinct shark species estimated to have an enormous size. Recently, Megalodon sharks have captured public curiosity and are even featured in many popular movies and TV shows, including The Meg, released in 2018.
Evidence of Megalodon
Very few traces of the Megalodon Shark have been left behind. Donald R. Prothero, a renowned paleontologist, explains, “Only teeth and a few partially mineralized backbones of C. megalodon have been found.” Sharks’ cartilage degrades and does not leave much information in the fossil record. As a result, most of the information about Megalodon comes from comparing fossilized teeth to modern sharks.
Megalodon’s size and weight are extrapolated, based on the similarities of Megalodon with Great White Sharks or, more recently, smaller Mako Sharks.
Great White Sharks grow up to 20 feet (6.1 meters) long and are considered a vulnerable species. In comparison, the Mako Shark's average size is 6 to 7 feet (1.8-2.1 meters). There’s a big size difference between these sharks. The trouble with getting a reasonable size estimate for Megalodon is that it comes down to the teeth. Megalodon teeth are similar to Mako Shark teeth, but estimating size from teeth alone assumes that the rest of the shark is similar.
Bite marks on Baleen Whales and other small extinct whales have been attributed to Megalodon in some recent research, which may lead to more information in the coming years.
A team of Marine Biology researchers led by Dr. Craig R. McClain reported the largest shark ever reliably measured was an 18.8 meter (61.7 foot) Whale Shark. They believe sharks may not be able to exceed this size because cartilage is less supportive of internal organs than bone. However, another team of researchers led by Dr. Marianne E. Porter found that shark cartilage actually contains a higher volume of minerals and collagen, which allows the cartilage to be stiffer and behave more like bones
In any case, the majority of the estimates of Megalodon size are under 20 meters (66 feet). The Smithsonian Institute’s model is 52 feet (15.8 meters) long, and is intended to represent an average size female Megalodon (female sharks are larger than their male counterparts).
Megalodon would have been larger than the average Whale Shark, as female Whale Sharks average 14.5 meters (47.6 feet). Large Whale Sharks can reach Megalodon proportions. (Quick note, Whale Sharks are filter feeders, not toothy sea monsters.)
The upshot is, maybe Megalodon could have been bigger than 18 meters (60 feet). At this point, there’s no way to know for sure because there just aren’t enough fossils. The Whale Shark shows it is possible for sharks to reach large sizes.
The Shark Week Megalodon Scam
When I was a kid, I saw part of a documentary about an encounter with a megalodon, and I was excited to think it could still be found. It turns out all of the alleged encounters were faked to draw viewers. I am deeply annoyed by this, as for years, I assumed there could still be a few Megalodon around in unexplored deep-sea areas.
Paleontologist Donald R. Prothero writes, “Scientists and science journalists were horrified, and there was a huge backlash against the Discovery Channel for airing these ‘docu-fictions’ or ‘fake-umentaries’ and passing them off as fact. But it was probably to no avail – Megalodon: The Monster Shark Lives attracted 4.8 million viewers and became the most-watched show in the history of the network.”
Truth in reporting is important for reasons just like this.
If Megalodon is out swimming around somewhere, he’s doing an excellent job of hiding. Currently, there is no evidence to suggest a modern Megalodon exists, as cool as that would be. :(
If you do bench work in a life science lab, you’ve likely delved into molecular biology techniques (PCR, cloning, site-directed mutagenesis). Ask any scientist; they have a love/hate relationship with it. Love it when it works, hate it when it doesn’t.
Most biologists use PCR for two reasons:
1) Cloning: Amplifying an insert piece of DNA to subsequently cut with restriction enzymes and paste into a plasmid.
2) Quick changes (site-directed mutagenesis): Introducing a point mutation, small insert, or deletion into an intact plasmid
As a fifth-year PhD candidate, I’ve done my fair share of cloning and site-directed mutagenesis. Not to toot my own horn, but my success rate is pretty high. Why, you may ask? Here’s why:
1. Primer Design Hacks
2. PCR Hacks
So there you have it! My tips for increasing your PCR success rate. Give them a try!!
Bolded Science is turning two years old! Thank you to our writers and readers for supporting our science communication venture. We've hosted writers from six continents and produced over 80 blog posts!
As I wrap up my doctoral studies and accelerate my job hunt, the rate of new articles will begin to slow. Meanwhile, I welcome you to browse our Posts By Category and our Archives.
Kerry Silva McPherson
We are taking a short two-week break for the holiday. Meanwhile, we welcome you to check out our Posts By Category page to read some Bolded Science favorites.
Interested in guest writing for us? We have open publish dates in December. Please refer to our FAQ page and email us if you are interested.
Happy Thanksgiving to all those celebrating!!
Science has been an indispensable part of modern society for some time now. Indeed, science is a purveyor of our material betterment, and is being inevitably drawn into debates about the responsibilities of living in the world shaped by our scientific prowess. Inevitably, also, the cry of "Pseudoscience!" is heard ever more often, in accusation against perceived dishonest uses of science and its intellectual clout. We, the scientists, people with intimate knowledge of its workings, should pay attention to these developments.
There is no doubt that the methodology and spirit of science are abused and misused in the pursuit of goals unrelated to scientific truth. But these offenses are not all the same, so let us try to set up, however tentatively, a scale of dishonesty.
At the top of the scale are the efforts of industries to protect their economic interests from damaging scientific findings. Such efforts are highly dishonest, methodical, and well funded: an "institute" with a reputable-sounding name puts out press releases and white papers in scientific format and jargon, aiming to discredit or cast doubt on science that its sponsors find inconvenient. Tobacco industry has done this against the findings about health and smoking, and the fossil-fuel industry actively tries to discredit climate science. We could call such efforts actual pseudoscience, i.e. intentional deception cloaked as scientific work. They are difficult to counter, but greater public awareness can blunt their effects.
Next in rank is selective denialism, which is motivated by ideology rather than money. Main representatives are creationism and the flat-Earth movement; both are highly dishonest, given to misrepresenting and disregarding inconvenient evidence, and are characterized by a contrived, implacable "skepticism" toward a particular scientific topic, skepticism with which they obviously do not approach the rest of factual knowledge. Creationists will go so far as to "doubt" (without basis) the constant rate of radioactive decay, in order to subvert the timeline of the fossil record, and the flat-Earthers have constructed an entire fantastic alternative physics to accommodate their central proposition.
Next come spurious health and wellness claims which we could collectively call quackery. To the extent that quackery touches upon science, it is usually by vacuous "sciency" claims of cleansing and boosting something or another in your body. Quackery rarely damages the standing of good science, but it intentionally misappropriates the clout of science for unwarranted monetary gain.
In the fourth tier are beliefs we could call paranoid reasoning. For example, in the 1980s a tenacious belief developed that high-voltage power lines caused childhood leukemia. This belief was based on faulty analysis of the geographic distribution of leukemia cases, and on overblown fears of "radiation." Subsequent studies revealed no mechanism by which electromagnetic fields of power-line strength and frequency could cause cancers.
Similarly, contemporary anti-vaccine movement seems to be driven by exaggerated fears of minor risks posed by vaccines (as compared to risks of the actual diseases), fears supported by cherry-picked data. Like the selective denialism, this mindset practices an unassuageable skepticism in one narrow area, and even prides itself on scientific independent-mindedness for it. Hence the comical mantra "I did my own research!"
Paranoid reasoning is motivated by emotional rather than material gain. It is ostensibly well-intended, but its misuse of the scientific method in the justification of ill-founded fears can have tragic consequences.
And then there are inquiries that we should better describe as quixotic than pseudoscientific. Such are the occult and paranormal investigations, UFOs etc. The history of these efforts is rife with fraud and conspiracy mongering, they seldom have a plausible methodology, and they have turned up precious little of value over time. The deception in these activities seems to be mostly self-directed.
Some quixotic inquiries, however, may bring about future scientific benefits. The quest for "irreducible complexity" is an offshoot of creationism: its premise is that some biological systems require many parts working together; imperfection in any one of them would incapacitate the whole system. Consequently, such systems could not evolve by small iterative steps, and must be evidence of design by an intelligent entity.
To be fair, the molecular mechanism of DNA genetics fits that bill rather well. However, this mechanism could have plausibly evolved by "trellising" on a simpler RNA-based precursor, which could have in turn used an even simpler (hypothetically even mineral-based) replicator as a trellis. The irreducible complexity's proponents seek to prove that something is impossible without knowing the limits of what is possible, but they may yet stimulate better theoretical insights into the scope of the evolutionary process. After all, what are the laws of thermodynamics but a refutation of the quest for the perpetual motion machine!
In view of the above indignities, we scientists like to fall back on the "scientific method." There are different ways to define the scientific method, but we can broadly say that it requires empiricism (reliance on sensory evidence), and skepticism: every proposition is presumed false until confirmed by unambiguous evidence, and reasonable doubts are always due a consideration. But for all its proven strengths, scientific method is itself a heuristic, not an algorithm: it is justified because it works better than the alternatives (although notable criticisms were raised long ago), and its uniform application is limited by practical reasons:
[ An old, nerdy joke offers a wonderful illustration. Three scientists, traveling by train through Scottish countryside, spot a black sheep standing in a field:
"Look," cries the astronomer excitedly, "the sheep in Scotland are black!"
"No, my friend," cautions the experimental physicist, "we can only say that some sheep are black."
The mathematician gazes at the sheep, then proclaims: "In Scotland there exists at least one field, in which there exists at least one sheep, at least one side of which is black." ]
Looking at the scientific method from our mathematician's angle, we discover that the charge of pseudoscience could be leveled against surprisingly many things. Let's play the devil's advocate:
Are "soft" sciences – such as sociology or economics – pseudoscience? Their empirical evidence is rarely clear-cut, since fully controlled experiments are difficult, observations are sometimes limited to one-time events, and the studied systems are complex and only partially understood.
Is psychology? Almost all of psychology amounts to listening to people and observing their actions, little of it yielding anything "falsifiable" (e.g. per philosopher Karl Popper). Does this discipline therefore offer no meaningful knowledge, or is it fair to say that people are complicated and that psychology's conclusions are necessarily tentative?
Is brain science pseudoscience? It proposes, not unreasonably, to explain mental phenomena in terms of brain physiology, but as of today there is no substantive hypothesis that would spell out how EEG squiggles and MRI blotches might account for things like self-awareness, perception or will. Is this defensible science, or a quixotic quest oblivious of some fundamental difficulty?
In the "hard" sciences: is string theory pseudoscience? No currently feasible experiment can verify its predictions; all it has going for it is logical consistency and a promise of uniting gravity with other physical forces. Most physicists would call it an honest, unproven hypothesis.
Scientific method has unavoidable limitations, and we have to be prepared for the fact that dishonest actors will game these limitations to forward dishonest arguments. And the more complex the subject, the greater the opportunity to peddle falsehoods and quackery.
But let's also not be too quick with the accusation of pseudoscience. Scientific knowledge advances by reducing the scope of its limitations, by doing one more careful observation, by honestly addressing one more relevant objection – that is our best defense, and there is no single, canonical way of doing it. It goes very much against the scientific spirit to dismiss an inquiry merely because it is inexact, nascent or, worse, unfashionable.
Primary meaning of the prefix pseudo- (ψεύδω) is "to lie," to deceive intentionally. Deliberate self-serving lies, grift, and harmful self-deceptions are the real abuses of science, abuses committed for ulterior purposes. These are the things we should direct our indignation and our efforts against.
Getting involved in STEM outreach
Hello, readers. Kerry, here. I'm the creator of Bolded Science. In addition to heading this collaborative blog, a second science communication initiative I started is a STEM education outreach program at my university. Thus far, I've neglected to blog about my outreach and would like to rectify that problem today. I truly believe STEM outreach is an important enterprise that more, if not all, scientists should get involved in. If I can convince just one scientist to volunteer in outreach programs, I would consider this post a success!
What is STEM outreach?
STEM outreach is the act of educating communities that do not easily access STEM education or a specific subset of STEM education. I like to phrase it as scientists reaching outside of their laboratory bubble to engage with their communities.
Examples of outreach activities include:
Who does STEM outreach benefit?
STEM outreach can engage several populations. Although most outreach programs are geared towards classroom students, other groups that can benefit from STEM outreach are seniors, the general population, religious groups, after-school programs, and young scientists.
The scientific community also benefits tremendously from participating in STEM outreach. Firstly, STEM outreach reduces the mystique of science and promotes scientific literacy in the general population, thereby fostering trust between scientists and non-scientists. Secondly, it provides an opportunity for scientists to hone their science communication skills. Although most scientists are well-practiced at speaking academically, science communication to general audiences is a significant skill often neglected in our training. Lastly, volunteering is rewarding and fun! It's an excellent activity for your mental health as a researcher.
Why did I start a STEM outreach program at my school?
I research at a medical school campus that does not educate undergraduate students, meaning the graduate students are not teaching assistants. To provide teaching opportunities for graduate students, the university has a handful of programs that offer internships to high school and undergraduate students under the supervision of graduate students.
Some of my coworkers and I have participated in these programs. Thereafter, it became apparent that these programs partnered with schools from affluent backgrounds. In talking to some of the high school interns, I learned that many of the students were the children of professors, scientists, and doctors. They seemed to already have connections in the scientific field; half of them had previously interned for scientists or shadowed doctors.
It got me thinking, why are we specifically partnering with these schools? My university is geographically located between two cities primarily inhabited by the working class and people of color. Why are we not working with those schools?
A few students and I designed a program, Young Explorers in Science (YES for short), to reach out to school districts with lower socioeconomic and diverse backgrounds. We had three goals:
1. Bring the science to them: Perform hands-on experiments in classrooms.
2. Mentor: Host career and college discussions with high school students to guide their potential scientific careers.
3. Bring the students to us: Organize field trips for local high schools so they can see the laboratories and participate in activities. (Unfortunately, this initiative was canceled due to COVID).
Providing a near-peer experience.
Sometimes, I receive skepticism about graduate students giving career advice, and that full-fledged scientists are better for the job. However, YES intentionally enlists graduate students as volunteers because we provide a near-peer experience. K-12 students can envision themselves as graduate students much easier than being a tenured PI. Graduate students can more accurately recollect and advise on early career experiences such as transitioning from high school to college, navigating the financial burden of being a student, and choosing a major.
How you can volunteer in STEM outreach
Studies show that involvement in science outside of the classroom is correlated with students choosing to pursue a STEM career. Furthermore, early STEM experiences foster confidence and problem-solving skills in students. STEM outreach is a simple way for scientists to help nurture the pipeline of incoming scientists.
What I enjoy most about volunteering for YES is that small efforts can have large impacts on a students' perception of their future selves. It's hard to explain how it feels when you connect with a student in a meaningful way. So, I guess if you'd like to understand what that feeling is like, get out there and volunteer in STEM outreach yourself!
Happy Halloween! We’ve had enough horror lately. So, to commemorate the holiday, I’d like to share a not-so-scary story, the story of the often-feared field of biophysics.
I am enrolled in a graduate school with an umbrella program in biomedical science. My program has seven varying concentrations. I work alongside all sorts of scientists: chemists, geneticists, immunologists, microbiologists, even skeletal biologists. During seminars and poster presentations, it’s typical to overhear a non-biophysicist remark that biophysics is “overly complicated,” “over my head,” or “above my pay grade.”
What is biophysics?
Biophysics uses physical theories to explain, describe, and observe biological events. Biophysicists investigate the structure of biomolecules, the thermodynamics of biological reactions, enzyme kinetics, protein movement, and more! Ever used or heard of FRET, NMR, x-ray crystallography, cryo-EM, small-angle scattering, or isothermal titration calorimetry? Those are all biophysical techniques.
Note that biophysics, like all areas of science, overlaps considerably with other areas of expertise. So, if you’re a biologist, chemist, neuroscientist, or biochemist, you’ve likely been exposed to some level of biophysics.
What’s all the fuss?
Ok, let’s clear the air. Why are people afraid of biophysics?
1) Math. When I joined the biophysics department at my school, I was often told, “Well, I hope you’re good at math.” I’m ok at math, but most calculations I do are your typical molarity calculations. True, some biophysicists use a lot of math in their research, particularly those who work with method development, but many of us do a basic level of math expected of any scientist.
2) MIA undergrad biophysics courses. Biophysics courses aren’t required or offered for many science degrees. But just because you didn’t learn about biophysics in a classroom doesn’t mean the knowledge is unattainable now. In fact, I wasn’t introduced to biophysics until after undergrad.
3) Jargon. Plenty of biophysicists love jargon!! They have a fancy vocabulary and enjoy showing it off. So, if you attend a biophysics seminar, the speaker might scare you away with their esoteric knowledge. Don’t let them! Biophysics can be accessible if communicated in a clear, concise way.
4) Theory. As part of their training, biophysicists are expected to learn the techniques they use as well as the theory and equations behind them. If you don’t understand the theory or equations, don’t fret (pun intended)! You can benefit from biophysics if you understand why an experiment was conducted and what conclusions were made without acquiring a deep understanding of underlying theory.
Crossing over: Learning biophysics as a non-biophysicist.
The next time you come across a biophysics figure in a paper or have the opportunity to use a biophysical technique, don’t shy away.
1) Ask for help from your biophysical friends. When doing so, tell them when they are using big words and phrases you don’t understand. Likely, the jargon they are using isn’t necessary but comes second nature to them.
2) YouTube. YouTube is an excellent source for explaining biophysical techniques. Pro tip: click around until you come across a biophysicist who speaks at your level. Some videos are super theory-oriented, while others are made for practicality and accessibility.
3) Take your time. Learning about biophysical techniques is more time-consuming than learning how a western blot or a qPCR works. Begin by learning the basics of an experiment: what is done and what can be learned. Then, your knowledge can grow from there.
A message to my fellow biophysicists:
It’s cool to show off our impressive data sets, brag about our niche knowledge, and geek out over new techniques. But remember to be mindful of your audience. Yes, when we speak to each other, we have our own language. But when we speak to researchers out of our niche, we can explain our work in simple, understandable terms. Improving our communication can help our fellow researchers explore biophysics while bolstering the importance of biophysical funding and education in academia.
When I started graduate school, I was prepared for the classwork. I was even prepared for being a teaching assistant. I had been doing this type of work since high school.
I thought that I was prepared for research as well. I thought that my graduate school research would be similar to the research I had done in undergrad. Unfortunately, starting my research in graduate school was a wake-up call.
For the first time, working hard did not mean more success. I couldn’t just complete a set of requirements to succeed in my research. I was stuck at a point where I was willing to work hard, but I had no idea what I was doing. My time felt wasted when my results were a dead end.
Moving forward five years, I have earned my Ph.D., completed dozens of research projects, worked as a postdoc and research specialist, and published and presented many different research projects.
Here is a secret though! I am not special. I’m not a genius or some natural-born researcher. I am a person just like you who struggled with research, learned, failed, and became successful through the process.
Research isn’t hard.
The biggest myth I believed was that research is innately hard. If saying research isn’t hard makes you angry or want to stop reading, hear me out.
The reality is research doesn’t need to be a struggle, but the majority of us are self-taught researchers. Especially if you learned research in academia, you have likely never had any training in how to conduct research. Of course, you have received training in how to collect data in your field, but what about how to learn your field, develop ideas, analyze your data, or publish and present your findings?
Think about when you learned a task you now find simple. In my case, I think about tying my shoes. I was so frustrated that I couldn’t tie my shoes that I wore velcro shoes way past when I should have. Now imagine that you were never taught how to tie your shoes. How much more difficult would it be to tie your shoes? How many more times would you fail? How much longer would it take you to be comfortable?
The reason this is so important to realize is that many of us will put up with things for much longer than we need to because we believe this is just how research is.
If I told you something wasn’t supposed to be hard, would you ask for help sooner? Would you find a way to make it easier? Would you stop overthinking what you are doing when it feels too easy? Would you spend more time living your life instead of trying to feel productive?
I know I would. I know because I did! My research journey changed when I stopped focusing on the struggle and found ways to make my research easier. This included reaching out to others for help, creating systems to complete my research, and accepting that I was in a learning process.
Therefore, if you believe your research is supposed to be a struggle, start challenging this idea. Ask for help and find mentors that can help you.
Now, I want to share 5 important tips for your research journey that I have learned through my own journey.
5 Tips to Become More Successful At Research
While you may be on board about becoming an efficient worker, you may still wonder how to become more efficient. So let’s go step-by-step through a system that I created for myself, which has made me more productive while decreasing burnout.
Stop Pursuing the Sexy Idea
We all want to be the scientist that has the research ideas that win Nobel Prizes and inspire movies. Therefore, we will dismiss novel, feasible ideas because they are too easy or don’t feel new enough. The reality is that the sexy ideas movies are created after likely weren’t how those ideas started.
Most projects are making a one or two step gain to the field. Then, once you complete a few projects, you have created a truly novel discovery that you likely would have not thought of at the beginning of the first project.
So instead, take the research idea that you know how to complete and you know is novel in some aspect and run with it! Don’t dismiss an idea because it doesn’t feel novel enough!
Research Success Happens in Indistinguishable Steps
TV and movies always show research success as a montage of hard work followed by one groundbreaking discovery that happened just as it was needed! Think about the Imitation game, The Big Bang Theory, or A Beautiful Mind.
While we may understand that it is being dramatized for the story, we may also think that the best way to have research success is through late nights, writing on a whiteboard, and insane amounts of coffee.
I would love to have my life turned into a movie one day, but all of my research success would break this notion. My success in research came from completing one step after another.
But looking back, what mattered weren’t the long nights or the time spent staring at data or reading papers. It was the time that I spent doing the little things like collecting one set of data at a time, creating one graph and a time, and testing one failed idea after another.
Take joy from the process of completing your research knowing that every step you are taking will lead you to success.
Stop Expecting Answers from Raw Data
When I first started performing data analysis, I wanted to look at spectra and be able to create clear conclusions about my data. Unfortunately, I was severely disappointed when I had no idea what my raw data was telling me.
For the majority of fields, raw data will only give you an understanding of the quality of your data.
Once you have examined your raw data, many of us will feel lost about what we do next. In my case, I knew that I needed to complete more analysis, but I didn’t know where to go because I was expecting my raw data to tell me.
I learned to create a system where I did specific data analysis steps to visualize my raw data. This means that I created many different figures to determine what the conclusions of my data were before I started planning my research papers.
This makes it easy because I do not even start looking for conclusions until I have my data visualized. It’s just a simple, emotionless process of data analysis until I can actually start creating conclusions from my data.
Don’t Personalize Your Results
One of my labmates in grad school was down on their research for years, they kept questioning whether they even deserved a Ph.D.
They were down on themselves and wondered if their research was worth publishing. They had completed a massive amount of research, but the research had not yielded the results the advisor was expecting.
I couldn’t believe that they were questioning whether they deserved a Ph.D. or if their research should be published.
Then I asked a single question, “Do you think that your results are a reflection on you or your capability as a researcher?”
“Well, yeah…”, they answered.
It is so common to think that if we are a good enough researcher we will have life-changing data. You can’t take personally how the universe works. Your research is about discovering how the world works and our capabilities to manipulate it.
Therefore, your capabilities as a researcher are reflected only in how well, accurately, and ethically you made your discoveries, not in the discoveries themselves!
All Good Research Communication Starts with a Story
So once you have collected and analyzed your data, most of us will jump into trying to write our papers. Then, we get frustrated because we don’t know what we are writing!
The first time I tried to write a research article, I sat in a Starbucks for 8 hours trying to figure out how to write an introduction. After a full day, I had completed 1 paragraph. I spent most of my time looking at other papers, writing a sentence and then deleting it, and questioning whether I could actually do this.
Two years later, I was writing papers in a matter of hours. Not only could I write papers, I enjoyed writing them.
There were two tricks to developing scientific writing skills: (1) always start with a story and (2) understand the purpose of each section of your paper.
Once I complete my data analysis, the next step is to create a figure outline. A figure outline is just your figures that will be in your paper in the order that you will present them.
The key to this figure outline is that you should be able to explain your research story to another person by just looking at your figures. Once you can do this, all you need to do is write your story down to complete your research paper!
You can be successful in research. No, I don’t have to know who you are or your credentials for this statement to be true. I know you can, because if you are interested enough in research to get through this blog, you are interested enough to be successful.
Now, you may need to help along your journey, which is completely normal. You will have challenging and frustrating times in your journey. But you should challenge the beliefs that you just have to struggle through your research to be successful. You can make your research journey easier with these tips:
On the grain, competition between species reigns supreme. Many species appear to be capable of inhibiting L. kefiranofaciens, which keeps its abundance in check. Meanwhile, Lactococcus lactis, another member of the core community, produces toxins known as bacteriocins that target and strongly inhibit the growth of other members of the community.
Although the community on the grains remains stable throughout the fermentation process, the surrounding milk sees increases and declines in different members as the microbes move from a solid to a liquid environment. The researchers described the kefir grains as a "basecamp" from which the different microbes "colonize the milk in an orderly fashion." This migration happens in a set order of species, which appears to have a lot to do with the preferred diet of each group.
Next stop: Cooperation Station
Unlike the grain environment, the complex nutritional landscape of the milk pushes the microbes to cooperate rather than compete. First to colonize the milk is L. kefiranofaciens, which, as on the grain, is the most abundant species throughout the fermentation process. Surprisingly, despite its dominance, L. kefiranofaciens can't grow in milk on its own. Its growth has been suggested to be supported by rare species, many of which can grow in milk alone. These species break down proteins and sugars in the milk to produce smaller molecules, generally referred to as metabolites, that L. kefiranofaciens could use to grow.
The analysis of these metabolites, using several techniques collectively known as metabolomics, was crucial for resolving how and why all these microbes cooperate. By measuring the concentrations of key metabolites throughout the fermentation process, it became apparent that some of their patterns match the growth patterns of key kefir species. For example, the concentration of a key intermediate molecule that cells use to produce energy, called citrate, drops suddenly in the early stages of fermentation. This drop correlates with the growth of two kefir species, L. lactis, and Leuconostoc mesenteroides, which suggests that they could be using citrate to grow.
In the milk, cooperative interactions between species are far more common than competitive ones, indicating that there are benefits to be gleaned from the presence of others. For example, L. kefiranofaciens and L. mesenteroides share a mutually beneficial relationship in which the presence of one promotes the growth of the other. This partnership seems to be centered on cross-feeding between the two. L. kefiranofaciens breaks up proteins into amino acids that can be accessed by L. mesenteroides. In return, L. mesenteroides, now able to carry out fermentation, produces lactate which L. kefiranofaciens can consume.
The acid test
The production and accumulation of major fermentation products, including lactate and acetate, acidifies the milk and gives kefir its distinct, slightly sour taste. However, these products also play a role in regulating the growth of microbes in the process. They inhibit the growth of many species that are important in the early- to mid-stages of fermentation, including L. lactis, L. mesenteroides, and several rare species.
Meanwhile, other species appear to benefit from the accumulation of lactate and acetate. Metabolomics measurements reveal that different species of yeasts and Acetobacter bacteria begin to grow during a favorable window of lactate and acetate concentrations in the milk, indicating accumulation of these metabolites clear the way for late-growing species. Although, high concentrations of lactate and acetate would be unfavorable for everyone.
Like many of the other core species, Acetobacter fabarum cannot grow in milk on its own. Here, another beneficial cross-feeding interaction appears to be at play, with L. lactis, an earlier-growing species, providing lactate and specific amino acids for A. fabarum to consume. However, whether A. fabarum provides anything in return is not clear. In contrast to the mutualism between L. kefiranofaciens and L. mesenteroides, this interaction could be described as commensalism: one partner benefits, and the other is seemingly unaffected.
Studying interactions between microbes presents a near-limitless well of discovery, precisely because these interactions are everywhere and occur in many flavors: from cooperation to vicious competition, and everything in between. Humans also have the potential to leverage microbial communities for our benefit: for example, understanding how microbial communities associated with crop plants function could help us devise ways to increase hardiness and yields under environmental stress. One longer-term goal is to develop a comprehensive-enough understanding that we can build communities to perform specific tasks, whether that be degradation of pollutants in the environment, producing new chemical compounds, or improving our health.